Dsip and its biometrics appln

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Image Processing for Biometrics

Preprocessing of Biometric Traits

Dr. Vinayak Ashok Bharadi

Associate Professor & HOD

Information Technology Dept.

Thakur College of Engg. & Tech.

Kandivali (East), Mumbai -400101

Physiological Biometric Traits

FingerprintPalmprint

Finger Knuckle Prints

Face Iris

Other examples are Hand Vein, Hand Geometry, Facial Thermogram, Retina, DNA, Ear Geometry, Body Odour.

Behavioral Biometric Traits

Dynamic Signature

Keystroke Dynamics

Other Examples are Speech, Gait, Facial Emotions

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Key Stages in Digital Image Processing

Image

Acquisition

Image

Restoration

Morphological

Processing

Segmentation

Representation

& Description

Image

Enhancement

Object

Recognition

Problem Domain

Colour Image

Processing

Image

Compression

Key Stages in Digital Image Processing:Image Aquisition

Image

Acquisition

Image

Restoration

Morphological

Processing

Segmentation

Representation

& Description

Image

Enhancement

Object

Recognition

Problem Domain

Colour Image

Processing

Image

Compression

Key Stages in Digital Image Processing:Image Enhancement

Image

Acquisition

Image

Restoration

Morphological

Processing

Segmentation

Representation

& Description

Image

Enhancement

Object

Recognition

Problem Domain

Colour Image

Processing

Image

Compression

Key Stages in Digital Image Processing:Image Restoration

Image

Acquisition

Image

Restoration

Morphological

Processing

Segmentation

Representation

& Description

Image

Enhancement

Object

Recognition

Problem Domain

Colour Image

Processing

Image

Compression

Key Stages in Digital Image Processing:Morphological Processing

Image

Acquisition

Image

Restoration

Morphological

Processing

Segmentation

Representation

& Description

Image

Enhancement

Object

Recognition

Problem Domain

Colour Image

Processing

Image

Compression

Key Stages in Digital Image Processing:Segmentation

Image

Acquisition

Image

Restoration

Morphological

Processing

Segmentation

Representation

& Description

Image

Enhancement

Object

Recognition

Problem Domain

Colour Image

Processing

Image

Compression

Key Stages in Digital Image Processing:Object Recognition

Image

Acquisition

Image

Restoration

Morphological

Processing

Segmentation

Representation

& Description

Image

Enhancement

Object

Recognition

Problem Domain

Colour Image

Processing

Image

Compression

Key Stages in Digital Image Processing:Representation & Description

Image

Acquisition

Image

Restoration

Morphological

Processing

Segmentation

Representation

& Description

Image

Enhancement

Object

Recognition

Problem Domain

Colour Image

Processing

Image

Compression

Key Stages in Digital Image Processing:Image Compression

Image

Acquisition

Image

Restoration

Morphological

Processing

Segmentation

Representation

& Description

Image

Enhancement

Object

Recognition

Problem Domain

Colour Image

Processing

Image

Compression

Key Stages in Digital Image Processing:Colour Image Processing

Image

Acquisition

Image

Restoration

Morphological

Processing

Segmentation

Representation

& Description

Image

Enhancement

Object

Recognition

Problem Domain

Colour Image

Processing

Image

Compression

Biometric System Architecture 14

Gradient

Preprocessing

• The preprocessing is a multi-step process. Fingerprint Preprocessing Steps are as follows:

1. Smoothening Filter

2. Intensity Normalization

3. Orientation Field Estimation

4. Fingerprint Segmentation

5. Ridge Extraction / Core point Detection

6. Thinning / ROI Extraction

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Fingerprint Segmentation

Segmentation Process (a) Normalized Input Image (b) Gabor Magnitude

Feature Map (c) Segmented Fingerprint (d) Histogram for Gabor

Magnitude Feature map (Threshold value is 29)

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Fingerprint Segmentation

Segmentation Process (a) Normalized Input Image (b) Gabor Magnitude

Feature Map (c) Segmented Fingerprint (d) Histogram for Gabor

Magnitude Feature map (Threshold value is 29)

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Core point Detection

The proposed technique is based on multiple features extracted from a fingerprint the feature set includes

• Coherence of Grayscale Gradient.

• Poincare Index.

• Angular Coherence.

• Orientation Field Mask.

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Core point Detection Contd.

(a) Core Point Feature Vectors (b) Selected Fingerprint

(c) Fingerprint with Marked Core Point

ParameterFS88

Database

FVC

2002,2004

Fingerprints

with clear

Core point

Accuracy % 84 68 98

Average Error (Pixels) 5.57 6.13 2.50

Average Execution Time (ms) 500ms 490ms 520ms

Core point Detection Test Results

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Fingerprint Enrollment 21

Fingerprint Recognition using Kekre’sWavelets

• Correlation based Fingerprint Recognition is implemented

• Kekre’s Wavelets are used for texture feature extraction

• Fingerprints are decomposed up to file levels. Wavelet Energy is calculated foreach level of decomposition

• Relative Energy Entropy & Euclidian Distance is used for classification

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Feature Vectors

Kekre’s Wavelet Energy Feature Vector Plot (a) Normalized by Total

Energy (b) Normalized by Level-wise Energy

(a)

(b)

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FKP ROI Extraction

We can see that the Orientation field in (b) is forming a loop surrounding the

phalangeal joint which is highlighted by a square. The coherence is also low

at the joint area as shown in (c), darker colour indicate low coherence

Sum of Angle Difference Cosine

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FKP ROI Segmentation

Final Feature Map with Horizontal Projection of

Feature Map & Vertical Projection of Feature Map,

Coordinate system Showing location of X & Y-Axis

Coordinate system fitted to the Finger-

Knuckle print and corresponding Region

of interest Segmented (256X128 Pixels )

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Questions?

Vinayak.Bharadi@thakureducation.org

Thank you for your patient listening…